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A Hybrid Probabilistic Model for Evaluating and Simulating Human Error in Industrial Emergency Conditions (HEIE)

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Abstract

Over the years, many techniques have been developed for human reliability analysis (HRA). The main weakness of traditional HRA approaches is the use of a simple classification scheme without a link to a model of cognition in terms of mental processes. The present work is an attempt in this direction through a particular hybrid probabilistic model. The human error in industrial emergency model aims to develop an integrated methodological approach useful in critical infrastructures during an emergency condition. The proposed method, starting from the integration of existing techniques, develops a very flexible tool, able to take into account the main external and internal factors responsible of human error in emergency conditions. The model is able to estimate the evolution of human behavior and error following the evolution of the emergency scenario. The final result is a simulation model that calculates the contextualized human error probability, through which it is possible to estimate a realistic and detailed scenario of the conditions during the emergency management.

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Acknowledgments

This research represents a result of research activity carried out with the financial support of MiuR, namely PRIN 2012 “DIEM-SSP, Disasters and Emergencies Management for Safety and Security in industrial Plants”.

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Authors and Affiliations

  1. University of Naples “Parthenope”, Isola C4 - Centro Direzionale Napoli, 80143, Naples, Italy

    Antonella Petrillo

  2. University of Cassino and Southern Lazio, Via G. Di Biasio 43, 03043, Cassino, Italy

    Fabio De Felice, Domenico Falcone, Alessandro Silvestri & Federico Zomparelli

Authors
  1. Antonella Petrillo
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  2. Fabio De Felice
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  3. Domenico Falcone
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  4. Alessandro Silvestri
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  5. Federico Zomparelli
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Correspondence to Antonella Petrillo.

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Petrillo, A., De Felice, F., Falcone, D. et al. A Hybrid Probabilistic Model for Evaluating and Simulating Human Error in Industrial Emergency Conditions (HEIE). J Fail. Anal. and Preven. 17, 462–476 (2017). https://doi.org/10.1007/s11668-017-0262-y

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  • DOI: https://doi.org/10.1007/s11668-017-0262-y

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